Demands by individuals, researchers, and enterprises (e.g., network operators and service providers) for increased compute performance and storage capacity of network computing devices have resulted in various computing technologies developed to address those demands. For example, compute intensive applications, such as enterprise cloud-based applications (e.g., software as a service (SaaS) applications), data mining applications, data-driven modeling applications, scientific computation problem solving applications, etc., typically rely on complex, large-scale computing environments (e.g., high-performance computing (HPC) environments, cloud computing environments, etc.) to execute the compute intensive applications, as well as store voluminous amounts of data. Such large-scale computing environments can include tens of hundreds (e.g., enterprise systems) to tens of thousands (e.g., HPC systems) of multi-processor/multi-core network nodes connected via high-speed interconnects (e.g., fabric interconnects in a unified fabric).
As such, various network topologies (i.e., the arrangement of various elements of a network) have been developed to manage such complex large-scale computing environments, which include a number of network computing devices (e.g., routers, switches, compute/storage nodes, etc.). Multiple factors (e.g., performance, price, scalability, etc.) are typically used to determine which type of network topology, and the network computing devices thereof, is deployed for a given network. It should be appreciated that as the number of network computing devices increase, the number of connections therebetween increases, thereby increasing costs associated with the network, from both monetary and performance perspectives. Accordingly, the hierarchical topology of the network can have a positive or negative impact on the overall characteristics and performance realized by users of the network, as well as the scalability of the network.